Collaborative Research: CNS Core: Medium: Data Augmentation and Adaptive Learning for Next Generation Wireless Spectrum Systems

合作研究：CNS 核心：媒介：下一代无线频谱系统的数据增强和自适应学习

• 批准号: 2107190
• 负责人: Shiwen Mao
• 金额: $ 32万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107190&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Deep learning has shown great promise in solving many open challenges in wireless networking research and applications. Deep learning is data hungry, and one of the critical obstacles towards fulfilling its promise is facilitating the acquisition of sufficient amounts of data to train and validate deep learning models. The primary goal of this project is to devise innovative approaches that enable wireless researchers and practitioners to acquire data more efficiently at reduced cost and to utilize existing data more effectively. Findings from this project are expected to fuel future breakthroughs in wireless research by making deep learning models more widely applicable. By integrating research and education, the proposed work will provide excellent hands-on exercises, research, and educational opportunities for undergraduate and graduate students at the three collaborating universities. The project will leverage the existing diversity-related outreach programs at the three institutions to broaden participation from under-represented groups. A team of four investigators with complementary expertise from Auburn University, Temple University, and California State University, Sacramento will carry out a coherent research agenda consisting of the following four thrusts: (1) Spectrum data synthesis and augmentation aided by generative adversarial networks; (2) Exploiting historical and synthetic wireless networking data through novel transfer learning algorithms; (3) Characterizing the relationship between dataset size and performance; (4) Integrate, validate and apply approaches developed in the first three thrusts on spectrum database construction, RF spectrum anomaly detection, and transmitter classification. Thrusts 1-3 are application-agnostic and focused on studying fundamental concepts and techniques that facilitate the acquisition of sufficient amounts of wireless data, enable more effective utilization of existing data, and enable the prediction of how much data is needed to meet desired performance. Thrust 4 is application-specific and focused on specific wireless applications where deep learning has been applied and demonstrated great potential. The data, software and education materials developed from this project will be widely disseminated. The project will engage industry stakeholders on project-related issues, with the aim to disseminate ideas and learn relevant challenges faced by the industry when applying deep learning to wireless applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

深度学习在解决无线网络研究和应用中的许多开放挑战方面显示出了巨大的前景。深度学习需要数据，而实现其承诺的关键障碍之一是促进获取足够数量的数据来训练和验证深度学习模型。该项目的主要目标是设计创新方法，使无线研究人员和从业人员能够以更低的成本更有效地获取数据，并更有效地利用现有数据。该项目的研究结果预计将通过使深度学习模型更广泛地应用来推动无线研究的未来突破。通过整合研究和教育，拟议的工作将为三所合作大学的本科生和研究生提供极好的实践练习、研究和教育机会。该项目将利用三个机构现有的与多样性相关的外展计划，扩大代表性不足群体的参与。由来自奥本大学、天普大学和加州州立大学萨克拉门托分校的四名研究人员组成的团队将执行一个连贯的研究议程，包括以下四个重点：（1）生成对抗网络辅助的频谱数据合成和增强； (2) 通过新颖的迁移学习算法利用历史和综合无线网络数据； (3) 表征数据集大小与性能之间的关系； (4) 集成、验证和应用在频谱数据库构建、射频频谱异常检测和发射机分类的前三个重点中开发的方法。主旨 1-3 与应用程序无关，专注于研究基本概念和技术，这些概念和技术有助于获取足够数量的无线数据，能够更有效地利用现有数据，并能够预测需要多少数据才能满足所需的性能。 Thrust 4 是针对特定应用的，专注于深度学习已得到应用并展现出巨大潜力的特定无线应用。该项目开发的数据、软件和教育材料将得到广泛传播。该项目将让行业利益相关者参与项目相关问题，旨在传播想法并了解行业在将深度学习应用于无线应用时所面临的相关挑战。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

Meta-Pose: Environment-adaptive Human Skeleton Tracking with RFID

Meta-Pose：利用 RFID 进行环境自适应人体骨骼追踪

• DOI: 10.1109/globecom46510.2021.9685315
• 发表时间: 2021-12
• 期刊: IEEE GLOBECOM 2021
• 作者: Yang, Chao;Wang, Lingxiao;Wang, Xuyu;Mao, Shiwen
• 通讯作者: Mao, Shiwen

MapLoc: LSTM-Based Location Estimation Using Uncertainty Radio Maps

MapLoc：使用不确定性无线电地图进行基于 LSTM 的位置估计

• DOI: 10.1109/jiot.2023.3262619
• 发表时间: 2023-08
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Wang, Xiangyu;Yu, Zhitao;Mao, Shiwen;Zhang, Jian;Periaswamy, Senthilkumar C.;Patton, Justin
• 通讯作者: Patton, Justin

Adversarial Deep Learning for Indoor Localization With Channel State Information Tensors

使用通道状态信息张量进行室内定位的对抗性深度学习

• DOI: 10.1109/jiot.2022.3155562
• 发表时间: 2022-10
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Wang, Xiangyu;Wang, Xuyu;Mao, Shiwen;Zhang, Jian;Periaswamy, Senthilkumar C.;Patton, Justin
• 通讯作者: Patton, Justin

Data Augmentation for RFID-based 3D Human Pose Tracking

基于 RFID 的 3D 人体姿势跟踪的数据增强

• DOI: 10.1109/vtc2022-fall57202.2022.10013052
• 发表时间: 2022-09
• 期刊: Proc. IEEE VTC-Fall 2022
• 作者: Wang, Ziqi;Yang, Chao;Mao, Shiwen
• 通讯作者: Mao, Shiwen

RFID-based 3D human pose tracking: A subject generalization approach

基于 RFID 的 3D 人体姿势跟踪：一种主题概括方法

• DOI: 10.1016/j.dcan.2021.09.002
• 发表时间: 2021-09-01
• 期刊: Digit. Commun. Networks
• 作者: Chao Yang;Xuyu Wang;S. Mao
• 通讯作者: S. Mao